Data analytics helps different enterprises in deriving valuable insights based upon analysis of real time incoming data. There are various data analytics tools available in the art that are capable of processing input data to derive valuable insights or outcomes based on the analysis of input data. In one example, a data analytics tool may be employed in order to predict a suitable product for a consumer based upon past purchase behavior of other consumers having similar demographic profile as that of the consumer. Similarly, the data analytics tool may be employed in order to predict number of bookings expected on Railway/Airway reservation portals during peak traffic seasons. Furthermore, in another example, the data analytics tool may also be employed for analyzing transactions in capital market in order to recommend a stock suitable for investment by an investor.
Typically, the data analytics tool functions based on data analytics models (also referred hereinafter as Models) including, but not limited to, a Logistic Regression Model, a Regression Model, a Cluster Model, a Tree Model, a Neural Network Model and the like. The data analytics models are generated based on programming languages such as R, Knime and the like. It is to be noted that the programming languages, of different formats/conventions, are complex and therefore difficult to comprehend. Usually, a software developer/programmer may wish to analyze/comprehend the model in order to check whether any of the features may be updated/added/deleted. Further, the software developer/programmer may wish to update the weights associated with one or more features of the model. Hence, the comprehension/analytics of each of the models is desirable.
Conventionally, the data analytics models designed using different programming languages are converted into a PMML (Predictive Model Markup Language) for the purpose of standardization and simplicity of understanding. By using PMML, the data analytical models are translated into a one common format (i.e. XML format). Therefore, all the aforementioned data analytics models are represented in the common format (XML) based on the PMML specifications. The data analytics models represented in PMML format are also called as PMML models. However, there still exists technical challenge in visualizing and updating the XML format of the PMML models. This is because the XML file of the PMML models captures the model information in form of tags and text which is difficult to comprehend quickly considering the complexity of the logic of each of the aforementioned models.